On-demand streaming video traffic is managed by an adaptive bi-trate (ABR) algorithm whose job is to optimize quality of experience (QoE) for a single video session. ABR algorithms leave the question of sharing network resources up to transport-layer algorithms. We observe that as the internet gets faster relative to video streaming rates, this delegation of responsibility gives video traffic a burstier on-off traffic pattern. In this paper, we show we can substantially smooth video traffic to improve its interactions with the rest of the internet, while maintaining the same or better QoE for streaming video. We smooth video traffic with two design principles: application-informed pacing, which allows ABR algorithms to set an upper limit on packet-by-packet throughput, and by designing ABR algorithms that work with pacing. We propose a joint ABR and rate-control scheme, called Sammy, which selects both video quality and pacing rates. We implement our scheme and evaluate it at a large video streaming service. Our approach smooths video, making it a more friendly neighbor to other internet applications. One surprising result is that being friendlier requires no compromise for the video traffic: in large scale, production experiments, Sammy improves video QoE over an existing, extensively tested and tuned production ABR algorithm.

When developing a new networking algorithm, it is established practice to run a randomized experiment, or A/B test, to evaluate its performance. In an A/B test, traffic is randomly allocated between a treatment group, which uses the new algorithm, and a control group, which uses the existing algorithm. However, because networks are congested, both treatment and control traffic compete against each other for resources in a way that biases the outcome of these tests. This bias can have a surprisingly large effect; for example, in lab A/B tests with two widely used congestion control algorithms, the treatment appeared to deliver 150% higher throughput when used by a few flows, and 75% lower throughput when used by most flows---despite the fact that the two algorithms have identical throughput when used by all traffic.

Measuring Quality of Experience (QoE) and integrating these measurements into video streaming algorithms is a multi-faceted problem that fundamentally requires the design of comprehensive subjective QoE databases and objective QoE prediction models. To achieve this goal, we have recently designed the LIVE-NFLX-II database, a highly-realistic database which contains subjective QoE responses to various design dimensions, such as bitrate adaptation algorithms, network conditions and video content. Our database builds on recent advancements in content-adaptive encoding and incorporates actual network traces to capture realistic network variations on the client device. The new database focuses on low bandwidth conditions which are more challenging for bitrate adaptation algorithms, which often must navigate tradeoffs between rebuffering and video quality. Using our database, we study the effects of multiple streaming dimensions on user experience and evaluate video quality and quality of experience models and analyze their strengths and weaknesses. We believe that the tools introduced here will help inspire further progress on the development of perceptually-optimized client adaptation and video streaming strategies. The database is publicly available at http://live.ece.utexas.edu/research/LIVE_NFLX_II/live_nflx_plus.html.

The Adaptive bitrate algorithm (ABR) is an essential part of any HTTP-based video streaming service. Given the endless array of network environments, device capabilities, and content properties in a commercial setting, perfecting ABR remains challenging. To identify shortcomings effectively at a large scale, a scalable methodology is needed to evaluate ABR algorithms under various scenarios. The state-of-the-art method is to evaluate a production ABR retrospectively with an optimal ABR algorithm. However, optimal ABR is an NP-hard problem and therefore is costly to be deployed at a commercial scale. As a result, shortcomings in the field are often identified through manual inspection. The process is labor-intensive and often relies on experience and intuitions built from reviewing the characteristics of a large number of sessions. Motivated by our operational experience, in this paper we propose an efficient approximation for the optimal ABR problem, thus enabling large-scale deployment and benchmarking of production ABR algorithms.

Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we suggest an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask when capacity estimation is needed. We test the viability of this approach through a series of experiments spanning millions of real users in a commercial service. We start with a simple design which directly chooses the video rate based on the current buffer occupancy. Our own investigation reveals that capacity estimation is unnecessary in steady state; however using simple capacity estimation (based on immediate past throughput) is important during the startup phase, when the buffer itself is growing from empty. This approach allows us to reduce the rebuffer rate by 10-20% compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate, and a higher video rate in steady state.

Now that our smartphones have multiple interfaces (WiFi, 3G, 4G, etc.), we have preferences for which interfaces an application may use. We may prefer to stream video over WiFi because it is fast, but VoIP over 3G because it gives continued connectivity. We also have relative preferences, such as giving Netflix twice as much capacity as Dropbox. This means our mobile devices need to schedule packets in keeping with our preferences while making use of all the capacity available. This is the natural domain of fair queuing, and this paper is about the design of a packet scheduler to meet these requirements. We show that traditional fair queueing schedulers cannot take into account a user's preferences for some interfaces over others. We present a novel packet scheduler called miDRR that meets our needs by generalizing DRR for multiple interfaces. We demonstrate a prototype running in Linux and show that it works correctly and can easily run at the speeds we need.

Policy-makers, ISPs and content providers are locked in a debate about who can control the Internet traffic that flows into our homes. In this paper we argue that the user, not the ISP or the content provider, should decide how traffic is prioritized to and from the home. Home users know most about their preferences, and if they can express them well to the ISP, then both the ISP and user are better off. To test the idea we built a prototype that lets users express highlevel preferences that are translated to low-level semantics and used to control the network.

Today's commercial video streaming services use dynamic rate selection to provide a high-quality user experience. Most services host content on standard HTTP servers in CDNs, so rate selection must occur at the client. We measure three popular video streaming services -- Hulu, Netflix, and Vudu -- and find that accurate client-side bandwidth estimation above the HTTP layer is hard. As a result, rate selection based on inaccurate estimates can trigger a feedback loop, leading to undesirably variable and low-quality video. We call this phenomenon the "downward spiral effect", and we measure it on all three services, present insights into its root causes, and validate initial solutions to prevent it.

Determining how to transport delay-sensitive voice data has long been a problem in multimedia networking. The difficulty arises because voice and best-effort data are different by nature. It would not be fair to give priority to voice traffic and starve its best-effort counterpart; however, the voice data delivered might not be perceptible if each voice call is limited to the rate of an average TCP flow. To address the problem, we approach it from a user-centric perspective by tuning the voice data rate based on user satisfaction. Our contribution in this work is threefold. First, we investigate how Skype, the largest and fastest growing VoIP service on the Internet, adapts its voice data rate (i.e., the redundancy ratio) to network conditions. Second, by exploiting implementations of public domain codecs, we discover that Skype's mechanism is not really geared to user satisfaction. Third, based on a set of systematic experiments that quantify user satisfaction under different levels of packet loss and burstiness, we derive a concise model that allows user-centric redundancy control. The model can be easily incorporated into general VoIP services (not only Skype) to ensure consistent user satisfaction.